Modeling and simulation of task rescheduling strategy with resource substitution in cloud manufacturing

Area of Science:

• Manufacturing Systems Engineering
• Artificial Intelligence
• Operations Research

Background:

• Cloud manufacturing environments with multiple agents and regions are prone to disturbances.
• Task exceptions in cloud manufacturing necessitate rapid service task rescheduling.

Purpose of the Study:

• To propose and evaluate a multi-agent simulation approach for cloud manufacturing service processes and task rescheduling strategies.
• To assess the impact of system disturbances on service quality and adaptive rescheduling capabilities.

Main Methods:

• Developed a multi-agent simulation model for cloud manufacturing service processes.
• Designed simulation evaluation indices including quality of service and flexibility of service.
• Proposed internal and external transfer strategies for service providers, considering resource substitution.
• Conducted simulation experiments under dynamic environments for a complex electronic product.

Main Results:

• The external transfer strategy demonstrated superior quality of service and flexibility of service compared to internal transfer.
• Sensitivity analysis identified key parameters: substitute resource matching rate for internal transfer and logistics distance for external transfer.

Conclusions:

• Multi-agent simulation is effective for evaluating cloud manufacturing rescheduling strategies.
• External transfer strategies are recommended for enhancing service quality and flexibility in disturbed cloud manufacturing environments.